Configuration of an Optical Switch Fabric Using Machine Learning

2. The switch fabric, as in claim 1, where the machine learning process is performed by a deep neural network (DNN).

3. The switch fabric, as in claim 2, where the DNN is trained with a dataset, the dataset having a plurality of records, each record having output information for the optical switch fabric and switch settings for each of the optical switch elements.

4. The switch fabric, as in claim 3, where each record further comprises other information.

5. The switch fabric, as in claim 4, where the other information includes one or more of the following: operating temperature of the optical switch fabric, humidity, and ambient temperature.

6. The switch fabric, as in claim 1, where one or more of the optical switch elements is a 2×2 Mach-Zehnder switch (MZS).

7. The switch fabric, as in claim 6, where the bias control signal is applied to an optical phase shift control of the 2×2 MZS to control the switch setting.

8. The switch fabric, as in claim 6, where the machine learning process is performed by a deep neural network (DNN) trained with a dataset of a plurality of records, each record having output information, disambiguating information about the output information, and optical phase shifts as switch settings for each of the MZSs.

9. The switch fabric, as in claim 8, where the disambiguating information includes one or more of the following: derivatives of the output information, and representative information about the derivatives of the output information.

10. The switch fabric, as in claim 1, where the model parameters are updated during operation of the switch fabric.

11. The switch fabric, as in claim 10, where the model parameters are updated by changes to one or more outputs of a machine learning process.

13. The process, as in claim 12, where the machine learning system is a Deep Neural Network (DNN).

14. The process, as in claim 12, where the learned feature information includes output values of an optical switch fabric and the labels are a switch setting for each of one or more optical switch elements connected in a topology to form the optical switch fabric.

15. The process, as in claim 14, where the learned feature information further comprises other parameters.

16. The process, as in claim 14, where the output values are updated by a data acquisition system monitoring an operation of the optical switch fabric and steps a through g are repeated with the updated output values to produce a new set of model parameters.

17. The process, as in claim 14, where the switch setting is an optical phase shift for a Mach-Zehnder switch (MZS).

18. The process, as in claim 17, where the learned feature information further comprises disambiguating information about the output values.

19. The process, as in claim 12, where the learned feature information includes a switch setting for each of one or more optical switch elements connected in a topology to form the optical switch fabric and the labels are output values of an optical switch fabric associated with switch settings.